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In this paper,a local-learning algorithm for multi-agent is presented based on the fact that individual agent performs local perception and local interaction under group environment.As for in-dividual-learning,agent adopts greedy strategy to maximize its reward when interacting with envi-ronment.In group-learning,local interaction takes place between each two agents.A local-learning algorithm to choose and modify agents’ actions is proposed to improve the traditional Q-learning algorithm,respectively in the situations of zero-sum games and general-sum games with unique equi-librium or multi-equilibrium.And this local-learning algorithm is proved to be convergent and the computation complexity is lower than the Nash-Q.Additionally,through grid-game test,it is indicated that by using this local-learning algorithm,the local behaviors of agents can spread to globe.
In this paper, a local-learning algorithm for multi-agent is presented based on the fact that individual agent performs local perception and local interaction under group environment. As for in-dividual-learning, agent adopts greedy strategy to maximize its reward when interacting with envi-ronment.In group-learning, local interaction takes place between each two agents. A local-learning algorithm to choose and modify agents’ actions is proposed to improve the traditional Q-learning algorithm, respectively in the situations of zero-sum games and general-sum games with unique equi-librium or multi-equilibrium. And this local-learning algorithm is proved to be convergent and the computation complexity is lower than the Nash-Q.Additionally, through grid-game test, it is indicated that by using this local-learning algorithm, the local behaviors of agents can spread to globe.